2 | � Fourth Quarter 2013 (President’s Message continued from page 1)
Bibliographic record
Abstract
The IAEE has grown more than 40 % since 2006, from about 3,000 to over 4,200. It is good to see so many students at conferences- their number has more than doubled to over 800 since 2006. Our student members are the next generation and we rely on them to carry the Association to new heights, so I would encourage them remain members. The valuable professional and academic contacts that affords are increasingly important in this networked age. Strong financial reserves are critical if the Association is to continue to innovate, and the IAEE is fortunately in good financial shape. Funding bodies increasingly mandate open access publication and we are taking a clear lead in becoming a Green Open Access Journal. We consider this necessary, but we can only take on the financial risk because of prudent husbanding of our assets. It should make our journals more attractive, increasing author’s citation index (and that of our journals), even if it risks reducing the IAEE’s reprint revenue. The IAEE mission is to “advance the knowledge, understanding and application of economics across all aspects of energy and foster communication amongst energy concerned professionals. ” Our publications advance communication, and we are now looking to advance knowledge with Immediate Past President Lars Bergman launching the Energy Economics Education Initiative. This will start by documenting
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.222 | 0.182 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".